With COVID-19 continuing to rage around the world, there is a spread of epidemic-related information on social networking platforms. This phenomenon may inhibit or promote the scale of epidemic transmission. This study constructed a double-layer epidemic spreading–information dissemination network based on the movements of individuals across regions to analyze the dynamic evolution and coupling mechanism of information dissemination and epidemic transmission. We also proposed measures to control the spread of the epidemic by analyzing the factors affecting dynamic transmission. We constructed a state probability equation based on Markov chain theory and performed Monte Carlo simulations to demonstrate the interaction between information dissemination and epidemic transmission. The simulation results showed that the higher the information dissemination rate, the larger the scale of information dissemination and the smaller the scale of epidemic transmission. In addition, the higher the recovery rate of the epidemic or the lower the infection rate of the epidemic, the smaller the scale of information dissemination and the smaller the scale of epidemic transmission. Moreover, the greater the probability of individuals moving across regions, the larger the spread of the epidemic and information. Finally, the higher the probability of an individual taking preventive behavior, the smaller the spread of the epidemic and information. Therefore, it is possible to suppress epidemic spread by increasing the information dissemination rate, epidemic recovery rate, and probability of individuals taking preventive behavior, while also reducing the infection rate of the epidemic and appropriately implementing regional blockades.

In the year 2020, a new type of coronavirus pneumonia (COVID-19) swept through the world. As of July 20, 2020, COVID-19 was responsible for more than 16 million infections and 600,000 deaths in more than 200 countries and regions. The number of infections has continued to increase, seriously threatening human life and health. It has also had a significant impact on global economic development. In the information age, when an infectious disease spreads in a society, information about that disease will spread quickly through social platforms, such as Weibo, WeChat, Twitter, and Facebook. The spread of such information can cause changes in human behavior, which can affect the spread of the disease [

Early studies on epidemic transmission were based on using differential equations to construct infectious epidemic models [

Some scholars have considered the mobility of individuals and explored the interaction between information dissemination and epidemic transmission by constructing dynamic multilayer networks. For instance, Xia et al. [

To overcome these shortcomings, we studied the impact of both individual mobility and regionality on the dynamics of epidemic transmission and information dissemination. We constructed a double-layer epidemic spread–information dissemination network model to study the interaction between epidemic transmission and information dissemination when individuals move and when their distribution is regional. By analyzing the factors that affect the dynamic transmission, we propose measures to control the spread of the epidemic. In the coupled dynamics model, we used the Markov state transition equation to calculate the probability that an individual was in a certain state at different time steps. We also conducted Monte Carlo simulations to demonstrate the process of interaction between information dissemination and epidemic transmission. This research not only supplements previous dynamic network research, but it also provides suggestions for the formulation of infectious epidemic control measures.

The rest of this article is organized as follows. Section 2 introduces the constructed model. Section 3 presents the simulation results and results analysis. Section 4 offers the main conclusions and directions for future research.

This research mainly involves two models: (1) An epidemic spread–information dissemination network model that is used to describe the real-life social relationships between people and their physical contacts; and (2) an epidemic spread–information dissemination dynamics model that is used to describe the spread of information and the epidemic among the population and the process of individual state changes.

We constructed an epidemic spread–information dissemination coupling network by modifying the double-layer network model based on moving individuals proposed by Xia et al. [

The physical contact network of the epidemic layer is shown in _{i}

where

In this study, we assumed that the dynamics of information dissemination in social networks could be described by the SIR model. Based on this assumption, individuals in the information layer could be in one of three states: Susceptible state _{I}_{I}_{I}_{I}_{I}_{I}_{I}_{I}_{I}_{I}_{I}_{I}_{I}

The transmission dynamics of the epidemic can also be described by the SIR model, as shown in _{E}_{E}_{E}_{E}_{E}_{E}

The processes of information and epidemic transmissions are expressed by dynamic equations. Two vectors, _{I}_{I}_{I}_{E}_{E}_{E}

where,

_{ij}_{ij}_{ij}_{ij}

When calculating and simulating the model, at the initial moment, we randomly selected a certain percentage of individuals as infected people at the epidemic layer, and these individuals became infected at the information layer. The remaining individuals were susceptible both at the epidemic layer and the information layer. The states of individuals at different layers changed based on the information and epidemic transmission dynamics until there were no more infected people at the epidemic layer and the information layer.

In general, the population density of a region and the population density of an infected population were different. Therefore, we simulated the coupling and spread of information and the epidemic in a heterogeneous region. First, based on the BA model, we generated a social network with 1,000 nodes by introducing one node and three links into the network at each time step. To generate the corresponding physical contact network, we constructed a square area with a side length of

The simulation was first conducted using the following parameter values: _{I}_{I}

We also analyzed the convergence of the above model. We extracted the node density values of the information layer and the epidemic layer in each state at different time steps. If the density of nodes in each state eventually reaches a stable state over time, it indicates that the model is convergent. The convergence analysis results are presented in _{E}_{E}

10 | 30 | 50 | 70 | 90 | 110 | 130 | 150 | |
---|---|---|---|---|---|---|---|---|

0.145 | 0.125 | 0.125 | 0.125 | 0.125 | 0.125 | 0.125 | 0.125 | |

0.041 | 0.001 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |

0.814 | 0.874 | 0.875 | 0.875 | 0.875 | 0.875 | 0.875 | 0.875 | |

0.383 | 0.353 | 0.353 | 0.353 | 0.353 | 0.353 | 0.353 | 0.353 | |

0.356 | 0.052 | 0.006 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | |

0.261 | 0.595 | 0.641 | 0.647 | 0.647 | 0.647 | 0.647 | 0.647 |

(1) How does the probability of a node jumping across regions, i.e.,

First, we studied the dissemination of the epidemic and information for the case in which the cross-region jump probability of nodes in each region was the same. The values of the parameters were as follows: _{I}

Next, we studied the dissemination of the epidemic and information when the cross-region jump probability of each regional node was different. The parameters were set as follows: _{I}

(2) How do the information dissemination rate

We repeated the simulation by changing the information dissemination rate _{I}_{I}_{E}_{E}_{I}_{I}_{E}

Next, we conducted a simulation by changing the information recovery rate

(3) How do the epidemic transmission rate

We conducted this simulation by changing the epidemic transmission rate _{I}_{I}

Next, we conducted a simulation by changing the epidemic recovery rate _{I}

(4) How does the probability of taking preventive actions

Lastly, we conducted a simulation by changing the probability of taking preventive actions _{I}

Our analysis of the factors affecting epidemic transmission can provide a helpful scientific basis for epidemic prevention and control. This study considered the fact that people move around and tend to be active in fixed areas. We analyzed the dynamic evolution and coupling effect of information dissemination and epidemic transmission during the epidemic. We constructed a double-layer epidemic spread–information dissemination network in which the social network of the information layer was a static network. The network structure did not change over a short period of time. The physical contact network of the epidemic layer was a dynamic time-varying network that considered the mobility and regional activity of individuals. After constructing the double-layer network, we simulated the spread of information and the epidemic in the information and epidemic layers using the SIR model and studied the impact of several factors on epidemic transmission and information dissemination. The considered factors included different cross-regional jump probabilities, information dissemination rate, information recovery rate, epidemic transmission rate, epidemic recovery rate, and the probability of taking preventive actions.

Through these simulations, we found that increasing the rate of information dissemination could increase the peak density of information disseminators and the scale of information dissemination at the information layer, although this had an inhibitory effect on the spread of the epidemic. The inhibitory effect of the information layer on the epidemic layer was achieved primarily by enabling informed individuals to take preventive behaviors to reduce the probability of infection. The simulation results also showed that the higher the probability of taking preventive behavior, the more obvious the inhibitory effect on the epidemic. Therefore, in epidemic control, it is necessary to improve the dissemination of information and provide more people with information related to the epidemic. In addition, other measures are needed to increase the probability of individuals taking preventive behaviors, such as making the wearing of masks compulsory on certain occasions. The results also showed that the scale of the spread of information and epidemic was larger when people moved across regions over long distances than when they moved within their current region. Therefore, people should minimize cross-regional transit during an epidemic. Finally, with respect to lockdown measures, in areas with a high population density and high infection levels, the lockdown significantly helped to control the epidemic in our simulations. However, in areas with a small population density and relatively low infection levels, the blockade policy could be appropriately relaxed.

Although this study provides theoretical guidance for the prevention and control of the spread of epidemic, there is room for further improvements. In this work, we explored only the impact of a single influencing factor on the spread of information and the epidemic. The interactions between multiple influencing factors were not considered. In addition, we considered that an individual had only three states in the epidemic layer: Susceptible state, infected state, and recovery state. However, in real life, an individual can have other states, such as a latent state and an isolation state. Therefore, in future research, we will increase the number of individual states in the epidemic layer and study the impact of simultaneous changes in multiple influencing factors on the spread of information and the epidemic. In addition, deep learning has been shown to have excellent performance in solving complex problems [